GIS-Based Analysis of Changing Surface Water in Rajshahi City Corporation Area Using Support Vector Machine (SVM), Decision Tree & Random Forest Technique
Machine Learning Research
Volume 3, Issue 2, June 2018, Pages: 11-17
Received: Jul. 22, 2018;
Accepted: Aug. 1, 2018;
Published: Sep. 3, 2018
Views 1688 Downloads 560
Mahbina Akter Mim, Department of Computer Science and Engineering, Rajshahi University of Engineering & Technology, Rajshahi, Bangladesh
K. M. Shawkat Zamil, Department of Computer Science and Engineering, Rajshahi University of Engineering & Technology, Rajshahi, Bangladesh
Follow on us
Water is one of the essential natural resources of nature. All living creature depends on water. Living creatures are using water for their different purposes. Earth’s large portion is covered by salt water but very less has fresh water. Freshwater can be found as groundwater and surface water. Surface water is stored as waterbodies on the surface of this world. Ponds, canals, rivers, and lakes are some of the waterbodies that provide fresh water to us. These waterbodies are fulfilling our need for fresh water. Most of the waterbodies are drying up for natural disasters or they are continuously filling by humans. These resources need some of our attention to preserve them. Rajshahi Development Authority (RDA) and United States Geological Survey (USGS) provide important data for this research. Waterbodies are detected by using Geographic Information System (GIS), GIS gives us the power of mapping and store, detect, and manipulate spatial or geographic data. Images are collected from the Landsat 4-5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI). They are classified by using ArcGIS. Images are classified in maximum likelihood classification by generating signature files to extract feature. Percentage of waterbodies in each year is calculated from the attribute table. A dataset is prepared from these features and tested on different classification techniques. Support Vector Machine (SVM), Decision Tree and Random Forest Technique are implemented on this dataset. Among them, Random Forest shows 92% accuracy, which is better from other techniques. These algorithms also measure the precision, recall, and f1 scores of the classifiers. The precision, recall, and f1-score of random forest technique show 0.943, 0.920, 0.922, which indicate better accuracy than other techniques.
Waterbodies, GIS, Remote Sensing, ArcGIS, Maximum Likelihood Classification, Support Vector Machine (SVM), Decision Tree, Random Forest
To cite this article
Mahbina Akter Mim,
K. M. Shawkat Zamil,
GIS-Based Analysis of Changing Surface Water in Rajshahi City Corporation Area Using Support Vector Machine (SVM), Decision Tree & Random Forest Technique, Machine Learning Research.
Vol. 3, No. 2,
2018, pp. 11-17.
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Shiklomanov, Igor A. "Appraisal and assessment of world water resources." Water international 25.1 (2000): 11-32.
Md. Habibur Rahman. "Pond filling plagues Rajshahi city." DhakaTribune, 2 Sept. 2014, archive.dhakatribune.com/environment/2014/sep/02/pond-filling-plagues-rajshahi-city.
"Rajshahi City Corporation." BANGLAPEDIA, 9Mar. 2015, en.banglapedia.org/index.php?title=Rajshahi_City_Corporation.
Perlman, USGS Howard. "Groundwater depletion." Groundwater depletion, USGS water science, 9 Dec. 2016, water.usgs.gov/edu/gwdepletion.html.
Ahmeduzzaman, Mohammad, Shantanu Kar, and Abdullah Asad. "A Study on Ground Water Fluctuation at Barind Area, Rajshahi." International Journal of Engineering Research and Applications (IJERA) ISSN (2012): 2248-9622.
George, Geeja K., et al. "Study of Ground Water Pollution around an Industry Using GIS."
Melgani, Farid, and Lorenzo Bruzzone. "Classification of hyperspectral remote sensing images with support vector machines." IEEE Transactions on geoscience and remote sensing 42.8 (2004): 1778-1790.
"Landsat Program." Wikipidea, Wikimedia Function, 29 Nov. 2017, en.wikipidea.org/wiki/Landsat_program.
Natrella, Mary. "NIST/SEMATECH e-handbook of statistical methods." (2010).
Franc, Vojtech, and Václav Hlavác. "Multi-class support vector machine." Pattern Recognition, 2002. Proceedings. 16th International Conference on. Vol. 2. IEEE, 2002.
Patel, Savan. "Chapter 2: SVM (Support Vector Machine) - Theory – Machine Learning 101 – Medium." Medium, Machine Learning 101, 3 May 2017, medium.com/machine-learning-101/chapter-2-svm-support-vector-machine-theory-f0812effc72.
Hsu, Chih-Wei, Chih-Chung Chang, and Chih-Jen Lin. "A practical guide to support vector classification." (2003): 1-16.
Jin, Chen, Luo De-Lin, and Mu Fen-Xiang. "An improved ID3 decision tree algorithm." Computer Science & Education, 2009. ICCSE'09. 4th International Conference on. IEEE, 2009.
Breiman, Leo. "Random forests." Machine learning 45.1 (2001): 5-32.
Qi, Yanjun, Judith Klein-Seetharaman, and Ziv Bar-Joseph. "Random forest similarity for protein-protein interaction prediction from multiple sources." Biocomputing 2005. 2005. 531-542.
Kotsiantis, Sotiris B., I. Zaharakis, and P. Pintelas. "Supervised machine learning: A review of classification techniques." (2007): 3-24.
"Supervised and Unsupervised Classification in ArcGIS."GISGeography, 22 Jan. 2017, gisgeography.com/supervised-unsupervised-classification-arcgis/.